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5 cards from this section. Quick, good the night before a test.
What does it mean for data to be relevant?
Think about how irrelevant data would affect a prediction.
Why is accuracy important in data collection?
Try to remember how mistakes can lead to wrong predictions.
What characteristic of data ensures it helps solve relevant problems?
Consider what makes information applicable to your task.
True or False: Clean data is essential for accurate predictions.
Think about how dirty environments can affect clarity.
Imagine you are tasked with creating an AI model for predicting loan eligibility. Your dataset has significant gaps in data on applicants from different demographics. How would you address this issue while ensuring the model is fair?
Consider strategies that can improve data collection processes.
You find that your dataset contains many duplicates and errors, which seems to be affecting the model’s accuracy. List steps you would take to clean your data.
Focus on organizing and structuring your cleaning process.
6 more questions in this section. Enroll free to practise them all.
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